http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Sasanka Ghosh (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Sasanka Ghosh,Arijit Das 대한공간정보학회 2017 Spatial Information Research Vol.25 No.6
Large Indian urban agglomerations like Mumbai, Delhi, Kolkata and Chennai are expanding rapidly beyond their territorial limits. But census of India, the only source of official data related to urbanization in India is not capable of capturing rapid urban dynamic due to some loopholes of Census of India. DMSP/OLS night time image become an alternative source of data for urban growth monitoring using its capability of capturing emitted lights from different sources. A threshold-based approach is applied to eliminate non-urban light capturing pixels. Based on the threshold values of 31 urban areas of four megacities of India for 1992, 2001, 2011are extracted. Threshold-based DMSP/OLS night time image extracted urban areas of four megacities shows a rapid increase of urban areas in an around the megacities. Chennai and Mumbai show a declining trend from 1992–2001 to 2001–2011 time span whereas Delhi and Kolkata shows an increasing trend of urban areas from the same time period. Out of the four Indian megacities, Chennai shows the fastest growing megacities for both of this two time span. It is also found that Chennai and Delhi shows an urban area increase abound 60% in the 2001–2011 time span. Analysis of this study identifies the applicability of threshold-based approach for measuring the rapid urban growth of a big city using DMSP/OLS night time image which opens a new avenue for the urban researchers.
-
Sumit Das,Amitesh Gupta,Sasanka Ghosh 대한공간정보학회 2017 Spatial Information Research Vol.25 No.6
In this paper, an attempt has been made to delineate groundwater potential zones in Hingoli district, Maharashtra, India. Remote sensing and traditional data were collected from different sources and analysed in GIS software to prepare thematic maps of different geo-environmental factors such as lithology, drainage, lineaments, slope etc. as these factors having an impact on groundwater availability of an area, directly or indirectly. All these factors have been integrated into GIS software and multi influence factor method was applied to delineate groundwater potential zones. The result has shown about 50% area is having the good potentiality of groundwater, whereas about 6% region falls under very low potential area. The Central part of the study area is having very low groundwater potential, mainly due to the steep slope and rocky outcrop. The southern part of the Hingoli district has shown good groundwater potential, because of the gentle slope which influences water to infiltrate. All the major towns and villages are located in good groundwater prospect areas. Therefore, these locations do not experience extreme drought conditions. The present work is valuable for future planning and management of groundwater resource in Hingoli district.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
